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http://www.minituba.org

miniTUBA is a web-based modeling system that allows clinical and biomedical researchers to perform complex medical/clinical inference and prediction using dynamic Bayesian network analysis with temporal datasets. The software allows users to choose different analysis parameters (e.g. Markov lags and prior topology), and continuously update their data and refine their results. miniTUBA can make temporal predictions to suggest interventions based on an automated learning process pipeline using all data provided. Preliminary tests using synthetic data and laboratory research data indicate that miniTUBA accurately identifies regulatory network structures from temporal data. miniTUBA represents in a network view possible influences that occur between time varying variables in your dataset. For these networks of influence, miniTUBA predicts time courses of disease progression or response to therapies. minTUBA offers a probabilistic framework that is suitable for medical inference in datasets that are noisy. It conducts simulations and learning processes for predictive outcomes. The DBN analysis conducted by miniTUBA describes from variables that you specify how multiple measures at different time points in various variables influence each other. The DBN analysis then finds the probability of the model that best fits the data. A DBN analysis runs every combination of all the data; it examines a large space of possible relationships between variables, including linear, non-linear, and multi-state relationships; and it creates chains of causation, suggesting a sequence of events required to produce a particular outcome. Such chains of causation networks - are difficult to extract using other machine learning techniques. DBN then scores the resulting networks and ranks them in terms of how much structured information they contain compared to all possible models of the data. Models that fit well have higher scores. Output of a miniTUBA analysis provides the ten top-scoring networks of interacting influences that may be predictive of both disease progression and the impact of clinical interventions and probability tables for interpreting results. The DBN analysis that miniTUBA provides is especially good for biomedical experiments or clinical studies in which you collect data different time intervals. Applications of miniTUBA to biomedical problems include analyses of biomarkers and clinical datasets and other cases described on the miniTUBA website. To run a DBN with miniTUBA, you can set a number of parameters and constrain results by modifying structural priors (i.e. forcing or forbidding certain connections so that direction of influence reflects actual biological relationships). You can specify how to group variables into bins for analysis (called discretizing) and set the DBN execution time. You can also set and re-set the time lag to use in the analysis between the start of an event and the observation of its effect, and you can select to analyze only particular subsets of variables.

Proper citation: miniTUBA (RRID:SCR_003447) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://www.cidr.jhmi.edu/

Next generation sequencing and genotyping services provided to investigators working to discover genes that contribute to disease. On-site statistical geneticists provide insight into analysis issues as they relate to study design, data production and quality control. In addition, CIDR has a consulting agreement with the University of Washington Genetics Coordinating Center (GCC) to provide statistical and analytical support, most predominantly in the areas of GWAS data cleaning and methods development. Completed studies encompass over 175 phenotypes across 530 projects and 620,000 samples. The impact is evidenced by over 380 peer-reviewed papers published in 100 journals. Three pathways exist to access the CIDR genotyping facility: * NIH CIDR Program: The CIDR contract is funded by 14 NIH Institutes and provides genotyping and statistical genetic services to investigators approved for access through competitive peer review. An application is required for projects supported by the NIH CIDR Program. * The HTS Facility: The High Throughput Sequencing Facility, part of the Johns Hopkins Genetic Resources Core Facility, provides next generation sequencing services to internal JHU investigators and external scientists on a fee-for-service basis. * The JHU SNP Center: The SNP Center, part of the Johns Hopkins Genetic Resources Core Facility, provides genotyping to internal JHU investigators and external scientists on a fee-for-service basis. Data computation service is included to cover the statistical genetics services provided for investigators seeking to identify genes that contribute to human disease. Human Genotyping Services include SNP Genome Wide Association Studies, SNP Linkage Scans, Custom SNP Studies, Cancer Panel, MHC Panels, and Methylation Profiling. Mouse Genotyping Services include SNP Scans and Custom SNP Studies.

Proper citation: Center for Inherited Disease Research (RRID:SCR_007339) Copy   

•   Source: SciCrunch Registry

http://umcd.humanconnectomeproject.org

Web-based repository and analysis site for connectivity matrices that have been derived from neuroimaging data including different imaging modalities, subject groups, and studies. Users can analyze connectivity matrices that have been shared publicly and upload their own matrices to share or analyze privately.

Proper citation: USC Multimodal Connectivity Database (RRID:SCR_012809) Copy   

•   Source: SciCrunch Registry

http://brainarchitecture.org/

Evolving portal that will provide interactive tools and resources to allow researchers, clinicians, and students to discover, analyze, and visualize what is known about the brain's organization, and what the evidence is for that knowledge. This project has a current experimental focus: creating the first brainwide mesoscopic connectivity diagram in the mouse. Related efforts for the human brain currently focus on literature mining and an Online Brain Atlas Reconciliation Tool. The primary goal of the Brain Architecture Project is to assemble available knowledge about the structure of the nervous system, with an ultimate emphasis on the human CNS. Such information is currently scattered in research articles, textbooks, electronic databases and datasets, and even as samples on laboratory shelves. Pooling the knowledge across these heterogeneous materials - even simply getting to know what we know - is a complex challenge that requires an interdisciplinary approach and the contributions and support of the greater community. Their approach can be divided into 4 major thrusts: * Literature Curation and Text Mining * Computational Analysis * Resource Development * Experimental Efforts

Proper citation: Brain Architecture Project (RRID:SCR_004283) Copy   

•   Source: SciCrunch Registry

http://okcam.cbi.pku.edu.cn/ontology.php

CAMO (Cell Adhesion Molecule Ontology) is a set of standard vocabulary that provide a hierarchical description of cell adhesion molecules and their functions. We compiled a list for cell adhesion molecules by integrating Gene Ontology annotations, domain structure information, and keywords query against NCBI Entrez Gene annotations. Totally 496 unique human genes were identified to function as cell adhesion molecules, which is by far the most comprehensive dataset including cadherin, immunoglobulin/FNIII, integrin, neurexin, neuroligan, and catenin families. CAMO was constructed as a directed acyclic graph (DAG) using DAG-Edit to input, manage and update data. We annotated each term with name, definition and source references, as well as the relationship to other terms, based on manual reviews of domain architecture and functional annotations. If vertices represent terms and the relationships between terms are represented by edges, the terms in a DAG can be connected via a directed graph without cycles. CAMO thus provides a hierarchical description of functions of CAMs with five top-level categories: CAM gene families, CAM genetics, CAM regulation, CAM expression and CAM diseases. Each top-level term is further divided into several categories to describe the functions in detail.

Proper citation: CAMO - Cell Adhesion Molecule Ontology (RRID:SCR_004392) Copy   

•   Source: SciCrunch Registry

http://SchistoDB.net

SchistoDB is a genomic database for the parasitic organism Schistosoma mansoni, one of the major causative agents of schistosomiasis worldwide. It currently incorporates sequences and annotation for S. mansoni in a single user-friendly database. Several genomic scale analyses are available as well as ESTs, oligonucleotides, metabolic pathways and drugs. Make your data available: If you''d like to have your updates and/or datasets integrated in SchistoDB, drop us an email.

Proper citation: Schistosoma mansoni Database (RRID:SCR_004341) Copy   

•   Source: SciCrunch Registry

http://openneuro.org

Open platform for analyzing and sharing neuroimaging data from human brain imaging research studies. Brain Imaging Data Structure ( BIDS) compliant database. Formerly known as OpenfMRI. Data archives to hold magnetic resonance imaging data. Platform for sharing MRI, MEG, EEG, iEEG, and ECoG data.

Proper citation: OpenNeuro (RRID:SCR_005031) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/redcap-importer

Software that automates the process of retrieving and converting data to the format of a RedCap table and allows selection of directories and files for import.

Proper citation: redcap-importer (RRID:SCR_016032) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/FIONASITE

Software for uploading data to FIONA and capturing MR images and k-space data from medical image systems. It provides a web-interface to automate the data review (image viewer), integrate with the centralized electronic data record for assigning anonymized id's, and forward the data to the central archive.

Proper citation: FIONASITE (RRID:SCR_016012) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/Minimally-Processed-Image-Sharing

Software to share ABCD minimally processed data. It uploads minimally-processed MRI data to the NDA ( Non-Disclosure Agreement) ABCD (Adolescent Brain Cognitive Development) repository.

Proper citation: Minimally-Processed-Image-Sharing (RRID:SCR_016016) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/enroll

Software which provides a framework for the secure storage of Personal Identifyable Information (PII) for a multi-site longitudinal project centrally. Used in Adolescent Brain Cognitive Development (ABCD) Study.

Proper citation: enroll (RRID:SCR_016011) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/tick-tock

Software for research study observation that visualizes study related events per day. Any event generating function sends a 'tick' event to this application which will be visible on this applications web-interface.

Proper citation: tick-tock (RRID:SCR_016023) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/Fast-Track-Image-Sharing

Software for sharing the ABCD (Adolescent Brain Cognitive Development) study data on the National Data Archive (NDA).

Proper citation: Fast-Track-Image-Sharing (RRID:SCR_016021) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/eprime-data-clean

Software to convert E-Prime (software tool for psychology computerized experiment design, data collection, and analysis) generated files to CSV files without errors during conversion. The ABCD project is using E-Prime to run behavioral tests.

Proper citation: eprime-data-clean (RRID:SCR_016020) Copy   

•   Source: SciCrunch Registry

https://github.com/ABCD-STUDY/geocoding

Software that uses a geo-location database to determine individuals' residential environment in Adolescent Brain Cognitive Development (ABCD) study. It performs queries given individuals' residential history in longitude and latitude.

Proper citation: geocoding (RRID:SCR_016007) Copy   

•   Source: SciCrunch Registry

https://exrna-atlas.org

Software tool as data and metadata repository of Extracellular RNA Communication Consortium. Atlas includes small RNA sequencing and qPCR derived exRNA profiles from human and mouse biofluids. All RNAseq datasets are processed using version 4 of exceRpt small RNAseq pipeline. Atlas accepts submissions for RNAseq or qPCR data.

Proper citation: exRNA Atlas (RRID:SCR_017221) Copy   

•   Source: SciCrunch Registry

http://www.jneurosci.org/supplemental/18/12/4570/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 29, 2013. Supplemental data for the paper Changes in mitochondrial function resulting from synaptic activity in the rat hippocampal slice, by Vytautas P. Bindokas, Chong C. Lee, William F. Colmers, and Richard J. Miller that appears in the Journal of Neuroscience June 15, 1998. You can view digital movies of changes in fluorescence intensity by clicking on the title of interest.

Proper citation: Hippocampal Slice Wave Animations (RRID:SCR_008372) Copy   

•   Source: SciCrunch Registry

https://www.epimodel.org/

Software R package for mathematical modeling of infectious disease over networks. Provides tools for simulating and analyzing mathematical models of infectious disease dynamics. Mathematical Modeling of Infectious Disease Dynamics.

Proper citation: EpiModel (RRID:SCR_018539) Copy   

•   Source: SciCrunch Registry

http://rats.pub

Web service that conducts comprehensive literature mining to identify roles of genes in addiction. Searches PubMed to find abstracts containing genes of interest and list of curated addiction related keywords.

Proper citation: RatsPub (RRID:SCR_018905) Copy   

•   Source: SciCrunch Registry